Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study.

Background: Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues.

ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis.

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses.